GithubHelp home page GithubHelp logo

kailuokk / siamgat Goto Github PK

View Code? Open in Web Editor NEW

This project forked from ohhhyeahhh/siamgat

0.0 0.0 0.0 414 KB

Code for the paper "Graph Attention Tracking". (CVPR2021)

Home Page: https://openaccess.thecvf.com/content/CVPR2021/papers/Guo_Graph_Attention_Tracking_CVPR_2021_paper.pdf

Python 88.37% Makefile 0.09% C++ 4.31% C 3.45% Cython 3.79%

siamgat's Introduction

SiamGAT

1. Environment setup

This code has been tested on Ubuntu 16.04, Python 3.5, Pytorch 1.2.0, CUDA 9.0. Please install related libraries before running this code:

pip install -r requirements.txt

2. Test

Dataset SiamGAT Model Link
OTB100 Success 71.0 Google Driver/
BaiduYun(w1rs)
Precision 91.7
UAV123 Success 64.6
Precision 84.3
LaSOT Success 53.9 Google Driver/
BaiduYun(dilp)
Norm precision 63.3
Precision 53.0
GOT10k AO 63.1 Google Driver/
BaiduYun(zktx)
SR0.5 74.6
SR0.75 50.4
TrackingNet Success 75.3 Google Driver/
BaiduYun(n2sm)
Norm precision 80.7
Precision 69.8

Download testing datasets and put them into test_dataset directory. Jsons of commonly used datasets can be downloaded from BaiduYun. If you want to test the tracker on a new dataset, please refer to pysot-toolkit to set test_dataset.

The tracking result can be download from BaiduYun (extract code: 5o17) or GoogleDriver for comparision.

python testTracker.py \    
        --config ../experiments/siamgat_googlenet_otb_uav/config.yaml \
	--dataset UAV123 \                                 # dataset_name
	--snapshot snapshot/otb_uav_model.pth              # tracker_name

The testing result will be saved in the results/dataset_name/tracker_name directory.

3. Train

Prepare training datasets

Download the datasets:

Note: training_dataset/dataset_name/readme.md has listed detailed operations about how to generate training datasets.

Download pretrained backbones

Download pretrained backbones from link and put them into pretrained_models directory.

Train a model

To train the SiamGAT model, run train.py with the desired configs:

cd tools
python train.py

4. Evaluation

We provide the tracking results (extract code: 0wod) (results in Google driver) of GOT-10k, LaSOT, OTB100 and UAV123. If you want to evaluate the tracker on OTB100, UAV123 and LaSOT, please put those results into results directory. Evaluate GOT-10k on Server.
Get TrackingNet results from BaiduYun (extract code: iwlj), and evaluate it on Server.

python eval.py 	                          \
	--tracker_path ./results          \ # result path
	--dataset UAV123                  \ # dataset_name
	--tracker_prefix 'otb_uav_model'   # tracker_name

5. Acknowledgement

The code is implemented based on pysot and SiamCAR. We would like to express our sincere thanks to the contributors.

6. Cite

If you use SiamGAT in your work please cite our papers:

@article{cui2022joint,
title={Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks},
author={Cui, Ying and Guo, Dongyan and Shao, Yanyan and Wang, Zhenhua and Shen, Chunhua and Zhang, Liyan and Chen, Shengyong},
journal={International Journal of Computer Vision},
year={2022},
publisher={Springer},
doi = {10.1007/s11263-021-01559-4}
}

@InProceedings{Guo_2021_CVPR,
author = {Guo, Dongyan and Shao, Yanyan and Cui, Ying and Wang, Zhenhua and Zhang, Liyan and Shen, Chunhua},
title = {Graph Attention Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}

@InProceedings{Guo_2020_CVPR,
author = {Guo, Dongyan and Wang, Jun and Cui, Ying and Wang, Zhenhua and Chen, Shengyong},
title = {SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

siamgat's People

Contributors

capricorn231 avatar ohhhyeahhh avatar twotwo2 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.